Deep reinforcement learning-based heterogeneous graph neural networks for multi-task financial fraud detection: a new framework incorporating variational self-encoders

Financial fraud, as a global problem in the financial industry, brings huge economic losses to financial institutions and customers.In this paper, a multi-task financial fraud detection model is constructed based on heterogeneous graph neural network with deep reinforcement learning, combined with variational self-encoder.In this model, the variational self-encoder is combined with graph convolutional network to construct the node input representation coding module, as a way to enhance the multi-task financial fraud data and better mine the structured features of different nodes.The attention mechanism is then introduced to build the relation-aware attention, which deeply mines the input node features, further acquires the neighbor-generated features of different nodes in the network, and combines the mutual information to measure the nonlinear correlation between different random nodes.Then the financial fraud node representation is mapped into the highdimensional space by the multilayer perceptron, and then the financial fraud prediction confidence of the model is obtained, and different types of loss functions are set to ensure the detection efficiency of the model.The results show that the F1-macro and AUC values of the financial fraud detection model on the self-constructed FFD dataset are 0.749 and 0.925, respectively.Relying on the heterogeneous graphical neural network and the variational autocoder, a multi-task financial fraud detection model can be constructed, which provides a new idea for solving the suspected fraud and money laundering cases that may exist in the field of finance and economy.